Enhancing prostate cancer segmentation on multiparametric magnetic resonance imaging with background information and gland masks
A targeted and practical PCa segmentation method was designed, which could significantly improve PCa segmentation on MP-MRI by combining background information and gland masks. The Matt-Unet model showcased promising capabilities for effectively delineating PCa, enhancing the precision of MP-MRI analysis.
Authors
- Xiaobin Wei (ORCID: https://orcid.org/0000-0003-2260-4713)
- Shengdong Nie (ORCID: https://orcid.org/0000-0001-7825-4455)
- Shouqiang Jia
- Rong Sun (ORCID: https://orcid.org/0009-0008-6345-5228)
- Guangyu Wu
- Lei Wang (ORCID: https://orcid.org/0009-0006-8680-3737)
- Jie Chen (ORCID: https://orcid.org/0000-0001-7839-9806)
Institutions
- University of Shanghai for Science and Technology (CN)
- Shanghai Jiao Tong University (CN)
- Renji Hospital (CN)
- Shanghai Ninth People's Hospital (CN)
- Jinan City People's Hospital (CN)
- Shandong First Medical University (CN)
Publication Details
- Journal
- Medical Physics
- Published
- 2024-08-12
- DOI
- https://doi.org/10.1002/mp.17346
- Primary Topic
- Prostate Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Natural Science Foundation of Shanghai
- National Natural Science Foundation of China
- Shanghai Key Laboratory of Molecular Imaging